DSpace at Independent University, Bangladeshhttp://localhost:8080/xmlui
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Fri, 18 Jan 2019 03:49:47 GMT2019-01-18T03:49:47ZComputational Intelligence for Pattern Recognition in EEG Signalshttp://hdl.handle.net/11348/792
Computational Intelligence for Pattern Recognition in EEG Signals
Mutasim, Aunnoy K; Tipu, Rayhan Sardar; Bashar, M. Raihanul; Islam, Md Kafiul; Amin, M. Ashraful
Electroencephalography (EEG) captures brain signals from Scalp. If analyzed and patterns are recognized properly this has a high potential application in medicine, psychology, rehabilitation, and many other areas. However, EEG signals are inherently noise-prone, and it is not possible for human to see patterns in raw signals most of the time. Application of appropriate computational intelligence is must to make sense of the raw EEG signals. Moreover, if the signals are collected by a consumer grade wireless EEG acquisition device, the amount of interference is ever more complex to avoid, and it becomes impossible to see any sorts of pattern without proper use of computational intelligence to discover patterns. The objective of EEG based Brain-Computer Interface (BCI) systems is to extract specific signature of the brain activity and to translate them into command signals to control external devices or understand human brains action mechanism to stimuli. A typical BCI system is comprised of a Signal Processing module which can be further broken down into four submodules namely, Pre-processing, Feature Extraction, Feature Selection and Classification. Computational intelligence is the key to identify and extract features also to classify or discover discriminating characteristics in signals. In this chapter we present an overview how computational intelligence is used to discover patterns in brain signals. From our research we conclude that, since EEG signals are the outcome of a highly complex non-linear and non-stationary stochastic biological process which contain a wide variety of noises both from internal and external sources; thus, the use of computational intelligence is required at every step of an EEG-based BCI system starting from removing noises (using advanced signal processing techniques such as SWTSD, ICA, EMD, other than traditional filtering by identifying/exploiting different artifact/noise characteristics/patterns) through feature extraction and selection (by using unsupervised learning like PCA, SVD, etc.) and finally to classification (either supervised learning based classifier like SVM, probabilistic classifier like NB or unsupervised learning based classifiers like neural networks namely RBF, MLP, DBN, k-NN, etc.). And the usage of appropriate computational intelligence significantly improves the end results.
Part of the Studies in Computational Intelligence book series (SCI, volume 777)
Tue, 01 May 2018 00:00:00 GMThttp://hdl.handle.net/11348/7922018-05-01T00:00:00ZAnalysis and processing of in-vivo neural signal for artifact detection and removalhttp://hdl.handle.net/11348/791
Analysis and processing of in-vivo neural signal for artifact detection and removal
Islam, Md Kafiul; Tuan, Nguyen A; Zhou, Yin; Yang, Zhi
This paper analyses different types of artifacts that
appear in neural recording experiments and thus a method is
proposed to detect and remove artifacts as a part of
preprocessing procedures before information decoding. Through
modeling and data analysis, we reason that artifacts have
different spectrum statistics compared with field potentials and
spikes and the frequency bands of 150-400 Hz and >5 kHz are the
most prospective regions to detect artifacts. A synthesized
database based on recorded neural data and manually labeled
artifacts has been built to allow quantitative evaluations of the
proposed algorithm. Testing results have shown that over >80%
positive detection ratio is achievable for artifacts with magnitude
comparable to neural spikes. Quantitative signal-to-distortion
ratio (SDR) simulation has shown that it is possible to have 10-
30dB SDR improvement at waveform segments that contain
artifacts.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/11348/7912012-01-01T00:00:00ZA bio-inspired cooperative algorithm for distributed source localization with mobile nodeshttp://hdl.handle.net/11348/790
A bio-inspired cooperative algorithm for distributed source localization with mobile nodes
Khalili, Azam; Rastegarnia, Amir; Islam, Md Kafiul; Yang, Zhi
In this paper we propose an algorithm for distributed optimization in mobile nodes. Compared with many published works, an important consideration here is that the nodes do not know the cost function beforehand. Instead of decision-making based on linear combination of the neighbor estimates, the proposed algorithm relies on information-rich nodes that are iteratively identified. To quickly find these nodes, the algorithm adopts a larger step size during the initial iterations. The proposed algorithm can be used in many different applications, such as distributed odor source localization and mobile robots. Comparative simulation results are presented to support the proposed algorithm.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/11348/7902013-01-01T00:00:00ZINTERNATIONAL STUDENT POLICYhttp://hdl.handle.net/11348/789
INTERNATIONAL STUDENT POLICY
Independent University, Bangladesh
International students can benefit IUB in numerous ways (e.g., improving university
academic environment, dissemination of knowledge, improving the quality of IUB graduates, university image, and ranking, etc.). This policy provides overall guidelines regarding international students at IUB.
Sun, 18 Mar 2018 00:00:00 GMThttp://hdl.handle.net/11348/7892018-03-18T00:00:00Z